CTIN: Robust Contextual Transformer Network for Inertial Navigation

TL;DR

This paper introduces CTIN, a robust neural network combining ResNet and Transformer architectures to improve inertial navigation accuracy by effectively capturing spatial and temporal information from IMU data.

Contribution

The novel CTIN model integrates multi-head self-attention and multi-task learning to enhance inertial navigation performance over existing methods.

Findings

Outperforms state-of-the-art models on multiple datasets

Demonstrates robustness across diverse inertial datasets

Improves velocity and trajectory prediction accuracy

Abstract

Recently, data-driven inertial navigation approaches have demonstrated their capability of using well-trained neural networks to obtain accurate position estimates from inertial measurement units (IMU) measurements. In this paper, we propose a novel robust Contextual Transformer-based network for Inertial Navigation~(CTIN) to accurately predict velocity and trajectory. To this end, we first design a ResNet-based encoder enhanced by local and global multi-head self-attention to capture spatial contextual information from IMU measurements. Then we fuse these spatial representations with temporal knowledge by leveraging multi-head attention in the Transformer decoder. Finally, multi-task learning with uncertainty reduction is leveraged to improve learning efficiency and prediction accuracy of velocity and trajectory. Through extensive experiments over a wide range of inertial datasets~(e.g. RIDI, OxIOD, RoNIN, IDOL, and our own), CTIN is very robust and outperforms state-of-the-art models.